Weakly-Supervised Models Professors and Supervisors

Fahd JARADView profile

Professor

Çankaya UniversityTurkey

Prof. Fahd JARAD is a Professor in the Department of Mathematics at Çankaya University's Faculty of Arts and Sciences. With a strong academic background including a PhD from Bilkent University, he has established himself as a prominent researcher in fractional calculus and applied mathematics. His academic journey includes teaching courses such as Calculus and Introduction to Differential Equations. Education: PhD in Mathematics, Bilkent University (1996-2001) Master's in Mathematics, Bilkent University (1994-1996) Bachelor's in Mathematics, Middle East Technical University (1989-1993) Prof. JARAD's research primarily focuses on fractional calculus, differential equations, and mathematical modeling. His work spans theoretical developments in fractional operators and their applications to physical phenomena including fluid dynamics, heat transfer, and epidemiological modeling. He has made significant contributions to the theory of fractional derivatives, particularly the Caputo-Katugampola derivative, weighted fractional proportional operators, and conformable fractional calculus. His publication record shows a remarkable output of 277 publications, with a strong recent focus on fractional differential equations and their applications. The trends in his research indicate a growing interest in combining fractional calculus with real-world problems in fluid dynamics, epidemiology, and control theory. His work often bridges theoretical mathematics with practical applications, demonstrating the versatility of fractional calculus as a modeling tool. Scientific Recognition: Extensive publication record with 277 publications Active research in fractional calculus and its applications Significant contributions to fractional differential equations theory Prof. JARAD has supervised numerous graduate students, including 10 master's and doctoral candidates. His research has involved collaborations across multiple disciplines, particularly in applying mathematical models to physical and biological systems. He has also served as Department Head at Çankaya University from 2016-2018, demonstrating leadership in academic administration alongside his research and teaching responsibilities. His laboratory and research team focus on fractional calculus applications, differential equations, and mathematical modeling. Current work appears to be exploring novel fractional operators and their applications to complex physical systems, with particular emphasis on developing robust numerical methods for solving fractional differential equations.

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Huijuan XuView profile

Assistant Professor

Pennsylvania State UniversityUnited States

Huijuan Xu is an Assistant Professor in the Department of Computer Science and Engineering. Her research spans artificial intelligence, computer vision, and knowledge representation, with a focus on temporal modeling, semantic reasoning, and multimodal learning. She has contributed to advancements in virtual reality streaming, knowledge graph completion, and weakly-supervised video analysis. Research output: 32 publications (2015-2025), including 15 peer-reviewed articles and conference contributions Core research areas: Representation Learning (100% match), Knowledge Graph (100% match), Temporal Action Detection (86% match), and Motion Feature Learning (73% match) Her recent work explores: 2025 : Bandwidth-optimized VR streaming for edge devices 2024 : Neural concept reasoning for image retrieval and avatar generation from sparse data 2023 : Zero-shot scene graph generation and bias mitigation in visual QA

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Paul DellarView profile

Lecturer

University of OxfordUnited Kingdom

Paul Dellar is a Governing Body Fellow and Tutor in Applied Mathematics at Corpus Christi College, University of Oxford, where he also serves as a University Lecturer in Applied Mathematics. He returned to Oxford in 2007 after previous academic positions including a lectureship at Imperial College London and a Junior Research Fellowship at Corpus Christi College (2001–2004). His educational background includes undergraduate and graduate studies at the University of Cambridge, supplemented by participation in the Woods Hole Summer Study Programme in Geophysical Fluid Dynamics. Dr. Dellar's research spans several interconnected domains: Lattice Boltzmann methods : Developing computational frameworks for fluid dynamics, electromagnetic systems, and quantum applications, with industrial uses in automotive and nuclear engineering. Geophysical fluid dynamics : Deriving improved shallow water equations to model ocean currents (e.g., Antarctic Bottom Water crossing the equator) and atmospheric phenomena on planets like Jupiter. Multiscale modeling : Bridging kinetic theory, magnetohydrodynamics, and active matter systems through novel algorithms. His publications predominantly explore computational innovations in fluid dynamics and plasma physics, with recurring themes of lattice Boltzmann optimizations, conservation properties in geophysical models, and turbulence mechanisms in exotic systems. Recent work shows increasing focus on quantum lattice algorithms and high-precision magnetohydrodynamics solvers. Notable scientific recognition includes: Glasstone Research Fellowship (University of Oxford) He has supervised doctoral candidates such as Andrew Stewart, with whom he co-authored research on Coriolis force modeling in oceanography. Industrial collaborations include study groups addressing challenges like blood flow simulation for surgical stents and wave energy conversion systems.

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Cédric WemmertView profile

Professor

University of StrasbourgFrance

Cédric Wemmert is a Full Professor in Computer Science at the University of Strasbourg, where he heads the SDC team at the ICube laboratory (Engineering, Computer and Imaging Sciences Laboratory). He is a member of CNU 27 and teaches in the Computer Science department of the Technology Institute Robert Schuman. His research focuses on Data Science and Machine Learning , with key applications in: Digital Pathology (colon cancer, renal diseases) Air Pollution Modeling Network Analysis His recent publications demonstrate a strong trend in applying deep learning to histopathological image analysis, with contributions in stain invariant segmentation, virtual stain transfer, and colon cancer classification. His work bridges computer science and medicine, resulting in high-impact publications in journals like Knowledge-Based Systems and Artificial Intelligence in Medicine. Professor Wemmert actively supervises PhD researchers: Current PhD students: 4 Graduated PhD students: 11 His research is funded by: ANR (TURFU-Net, HistoGraph, AIR&D, CHOICED) INSERM (MAIA, IMPULSE) Regional bodies (ReSP-Ir, Grand-Est) He leads the SDC team at ICube, which develops machine learning and computer vision techniques for health, environmental, and urban infrastructure applications.

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Michael RohlfingView profile

Professor

University of MünsterGermany

Prof. Dr. Michael Rohlfing is a theoretical physicist at the Institute of Solid State Theory , University of Münster. His research focuses on electronic structure , optical excitations , and many-body perturbation theory in 2D materials and hybrid heterostructures . He leads the AG Rohlfing group, which develops and applies ab initio methods to study surfaces , interfaces , and nanoscale systems . Current projects include optical excitations in TMDC heterostructures under pressure (DFG since 2020) and electronic interface states in weakly bound systems (SFB 103, A13). His group investigates phenomena such as excitonic effects , electron-phonon interactions , and spin-orbit-driven surface states , with applications in nanotechnology and optoelectronics . Recent work explores trions , image potential effects , and valley-selective interlayer coupling in materials like CrSBr and MoS2 . Key publications highlight exciton dynamics in 2D systems, strain-tunable optical properties , and first-principles simulations of scanning tunneling microscopy images. His research team is involved in the Collaborative Research Center 1083 (Structure and Dynamics of Internal Interfaces) and the Computational 2D Materials Database (C2DB) . Scientific awards include the Heisenberg Fellowship (DFG, 2001-2004) and recognition for STM simulations and electron correlation studies . Prof. Rohlfing supervises numerous Bachelor’s , Master’s , and PhD students , including recipients of the Infineon PhD Award (Matthias Drüppel, 2017). His group regularly contributes to public workshops and collaborative experimental-theoretical studies .

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Thomas WendlerView profile

Professor

Technical University of MunichGermany

Thomas Wendler is a Professor at the Chair of Computer Aided Medical Procedures & Augmented Reality at Technische Universität München (TUM). His research focuses on the intersection of Artificial Intelligence, Medical Imaging, and Robotics, with specific interests in Longitudinal Image Analysis, Image-Guided Interventions, and Dosimetry for Radioisotope Therapy. Current affiliation: TUM Institute of Informatics Former leadership: Director of the Interdisciplinary Research Lab (IFL) Research Interests: Thomas Wendler's work bridges Artificial Intelligence Applications in Medicine with Robotic Imaging , emphasizing Longitudinal Image Analysis for disease progression and Image-Guided Interventions in clinical settings. His technical expertise includes Dosimetry for nuclear medicine and 3D Computer Vision for surgical applications. Teaching: He actively contributes to TUM's curriculum through lectures and practical courses such as: Computer Aided Medical Procedures I Medical Augmented Reality Introduction to Surgical Robotics Foundations in 3D Computer Vision Collaborations: Wendler collaborates with Prof. Nassir Navab and researchers like Mohammad Farid Azampour, Francesca De Benetti, and Zhongliang Jiang. His work has been published in journals including IEEE Transactions on Medical Imaging and Medical Image Analysis , with recent advancements in robotic ultrasound navigation and catheter tracking methodologies.

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Ali KarakusView profile

Associate Professor

Middle East Technical UniversityTurkey

Ali Karakus is an Associate Professor at Middle East Technical University (METU) and Deputy Head of Department. He obtained his Bachelor's degree (2005), Master's degree (2009), and PhD (2015) from METU. His research focuses on computational fluid dynamics, high-order numerical algorithms, and GPU-accelerated solvers for multiphysics problems. Specializes in high-performance scientific computing and finite element methods. Develops GPU-optimized solvers for compressible/incompressible flows, level-set reinitialization, and physics-informed neural networks. The Accelerated Multiphysics (AMiP) research group, led by Dr. Karakus, emphasizes discontinuous Galerkin approaches, adaptive mesh refinement , and Jacobian-free Newton-Krylov methods . His recent work integrates data-driven modeling with traditional fluid dynamics solvers. Notable scientific achievements include: 2024 Lecturer of the Year award from METU Parlar Foundation Collaborative publications on NekRS , a GPU-accelerated Navier-Stokes solver Selected as a groundbreaking paper in Parallel Computing journal Dr. Karakus has supervised multiple M.Sc. theses on topics including: High-order Boltzmann solutions for low Mach aerodynamics Physics-informed neural networks in CFD GPU-accelerated level-set reinitialization His publications demonstrate expertise in GPU parallelization , mesh deformation , and weakly compressible flow modeling .

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Andrew M NaidechView profile

Professor

Northwestern UniversityUnited States

Andrew M Naidech is Professor at Northwestern University's Feinberg School of Medicine with appointments across multiple departments including Neurology (Neurocritical Care), Medical Social Sciences (Outcome and Measurement Science), Preventive Medicine (Biostatistics and Informatics), Anesthesiology, and Neurological Surgery. He serves as leader of the Master of Public Health program and directs research at the intersection of neurocritical care and artificial intelligence. Dr. Naidech earned his MD from Temple University (1997) and MSPH from Tulane University (2001), completed residency at Tulane University Hospital & Clinics (2002), and fellowship at Columbia Presbyterian Hospital (2004). He holds board certifications in Neurology, Vascular Neurology, Clinical Informatics, and Neurocritical Care from the American Board of Psychiatry and Neurology. His research focuses primarily on intracerebral hemorrhage, with significant contributions to understanding hematoma expansion mechanisms, developing AI applications for stroke management, and analyzing critical care outcomes. Recent publications demonstrate a strong emphasis on machine learning for medical image analysis, thromboelastography biomarkers, and patient-centered AI implementation in stroke care. Dr. Naidech has received prestigious honors including Fellowship in the American Academy of Neurology (2017) and election to Fellowship in the American Neurological Association (2012). He maintains active professional service as Member of American Schools and Programs of Public Health (2024-present), MCCKAP committee member at Society of Critical Care Medicine (2012-present), and previously served on FDA's Peripheral and Central Nervous System Committee (2010-2019). Fellowship, American Academy of Neurology (2017) Fellowship (elected), American Neurological Association (2012) AOA - Tulane University, Alpha Omega Alpha Honor Medical Society (2001) He contributes significantly to editorial work as former Editorial Board Member of Neurocritical Care (2015-2020) and maintains active membership in multiple professional societies including the American Neurological Association, American Academy of Neurology, American Heart Association, Neurocritical Care Society, and Society of Critical Care Medicine. Dr. Naidech's work is supported through affiliations with the Institute for Augmented Intelligence in Medicine, Center for Health Services & Outcomes Research, and Northwestern University Clinical and Translational Sciences Institute.

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Ben CardoenView profile

Research Fellow

University of BirminghamUnited Kingdom

Dr Ben Cardoen is a Research Fellow at the School of Mathematics, University of Birmingham, specializing in algorithm development for biomedical imaging analysis at multiple scales—from diffuse optical tomography to superresolution microscopy. His work focuses on recovering minimal causal signatures for pathologies in ageing and inflammation using sparse high-dimensional data. His educational background includes: BSc in Computer Science, University of Antwerp (2015) MSc in Computer Science, University of Antwerp (2017) PhD in Computing Science, Simon Fraser University (2024) Following postdoctoral work at the University of British Columbia (2025), he joined the University of Birmingham. Dr Cardoen designs scalable, interpretable algorithms leveraging belief theory, graph algorithms, and structural causal discovery to push beyond empirical resolution limits in imaging modalities. His research targets degenerative diseases (Alzheimer, ALS, ageing), metabolic disorders (diabetes), and infectious diseases, with emphasis on robustness to complex noise models. Key methodologies include weakly supervised learning for protein-organelle interaction analysis in volumetric point cloud data, utilizing distributed computing on SLURM-based supercomputers. No scientific awards are documented in the provided materials. Regarding academic mentorship and funding, the text indicates no formal advising roles or grant details beyond his current fellowship position. Dr Cardoen's active projects involve extending probabilistic learning algorithms for nested label recovery in multichannel superresolution microscopy, with applications spanning genomic analysis, viral infection tracking, and cellular drug response modeling.

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Jun LiView profile

Senior Lecturer

University of Technology SydneyAustralia

Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.

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Mayur NaikView profile

Professor

National and Kapodistrian University of AthensGreece

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He holds office in Room 642B, Amy Gutmann Hall and maintains an active research program focused on the intersection of programming languages and artificial intelligence. Before joining UPenn, he was faculty at Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. Naik received his PhD in Computer Science from Stanford University in 2008 under Alex Aiken, a Masters from Purdue University in 2003 under Jens Palsberg, and a Bachelors from BITS Pilani in 1999. He grew up in Goa, India. His primary research interests center around neurosymbolic programming, which combines symbolic reasoning with machine learning to create more accurate, interpretable, and domain-aware AI systems. His group develops language design, learning algorithms, and compiler optimizations in this space, with their most mature effort being the Scallop neurosymbolic programming language and compiler toolchain. He also conducts research in trustworthy AI for healthcare applications and AI-enabled programming tools that improve programmer productivity. Analysis of his recent publications shows a strong trend toward neurosymbolic programming frameworks (Scallop, TorchQL), LLM-assisted program analysis (IRIS), and applications of these techniques to security, healthcare, and computer vision. His work consistently bridges theoretical foundations with practical implementations, often releasing open-source systems. Misra Family Professor (endowed chair, effective July 2024) Multiple distinguished paper awards (PLDI 2019, FSE 2015, PLDI 2014) Test-of-Time Paper Awards (FSE 2013, FSE 2012, EuroSys 2011) His student Elizabeth Dinella won the 2025 ACM SIGSOFT Outstanding Dissertation award Naik has advised numerous PhD students who have gone on to faculty positions at top institutions including Peking University, University of Toronto, Ashoka University, Bryn Mawr College, and Johns Hopkins University. His research is supported by grants from NSF, Google, Amazon, and other industry partners. His lab maintains active collaborations with clinicians and bioinformatics researchers to apply neurosymbolic programming to healthcare problems. His research group, which includes current PhD students and postdocs, develops practical open-source systems and applies them to diverse domains including computer vision, cybersecurity, medicine, and bioinformatics. The group maintains strong industry connections with Google, Microsoft, Amazon, and other tech companies.

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